Model based Fault Detection and Robust Steering Control Algorithm for Autonomous Vehicle Using Sliding Mode Observer

Oh, Kwangseok; Yi, Kyongsu

doi:10.7467/ksae.2017.25.6.732

Cited by 3 publications

(2 citation statements)

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“…Because the optimization goal of the VDT problem is to minimize the communication overhead of vehicle‐side data transmission, the reduction rate of communication overhead is the most important indicator to evaluate the performance of each optimization strategy 18 . This paper uses

italicreduction_{rate}_{s}

to define the respective reduction ratios of various sensor data, and the communication overhead reduction rate

italicreduction_{rate}_{s}

for various sensor data can be expressed as follows:

italicreduction_{rate}_{s} goodbreak= \frac{\sum_{i = 1}^{I} \sum_{j = 1}^{J} ((), c_{i, j, s} U_{i, j, s} L_{i, j})}{\sum_{i = 1}^{I} \sum_{j = 1}^{J} ((), c_{i, j, s} L_{i, j})}

…”

Section: Dynamic Management Network Methods For Vehicle Detection Bas...mentioning

confidence: 99%

“…Because the optimization goal of the VDT problem is to minimize the communication overhead of vehicle-side data transmission, the reduction rate of communication overhead is the most important indicator to evaluate the performance of each optimization strategy. 18 This paper uses reduction_rate s to define the respective reduction ratios of various sensor data, and the communication overhead reduction rate reduction_rate s for various sensor data can be expressed as follows: A reduction rate of rduction_rate total is used to define the reduction rate of the total communication overhead including all types of sensors, both of which can be calculated by dividing the communication overhead of sending part of the data by the communication overhead of sending the full sampled data. 19 For the reduction rate rduction_rate total of the total communication overhead, it can be expressed as follows:…”

Section: Edge Computing Communication Overhead and Data Accuracymentioning

confidence: 99%

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Dynamic management network system of automobile detection applying edge computing

Cao

2023

Int J Network Mgmt

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Dynamic vehicle detection requires the transmission of large amounts of data collected by different types of sensors to the edge computing nodes. This is likely to cause network delays and congestion, affecting the computation of the edge computing nodes and thus posing serious security risks. Therefore, optimizing data transmission between vehicles and edge computing nodes is a new challenge to be addressed in the practical application of edge computing‐based vehicle dynamic detection architectures. The data requirements of VDT for vehicle detection dynamic detection in different environments are considered, the optimization objectives and constraints are analysed, and a deviation detection and greedy algorithm is proposed in this paper to address the problems of long mixed‐integer linear programme solution time and insufficient practical applications, and the performance of the algorithm is evaluated through simulation experiments conducted by simulation of urban mobility, a traffic flow simulation tool, and PreScan, a vehicle simulation test software. The results show that compared with the deviation detection algorithm, the greedy algorithm can reduce the communication overhead by 82.6%–86.2% in all cases and improve the performance by 13.6%–19.5%, which is more suitable for practical applications. The results of this paper contribute to the automation and modernization of vehicle technology management and information transfer.

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